@inproceedings{hasan-etal-2023-curtin,
title = "Curtin {OCAI} at {WASSA} 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers",
author = "Hasan, Md Rakibul and
Hossain, Md Zakir and
Gedeon, Tom and
Soon, Susannah and
Rahman, Shafin",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.47",
doi = "10.18653/v1/2023.wassa-1.47",
pages = "536--541",
abstract = "The WASSA 2023 shared task on predicting empathy, emotion and other personality traits consists of essays, conversations and articles in textual form and participants{'} demographic information in numerical form. To address the tasks, our contributions include (1) converting numerical information into meaningful text information using appropriate templates, (2) summarising lengthy articles, and (3) augmenting training data by paraphrasing. To achieve these contributions, we leveraged two separate T5-based pre-trained transformers. We then fine-tuned pre-trained BERT, DistilBERT and ALBERT for predicting empathy and personality traits. We used the Optuna hyperparameter optimisation framework to fine-tune learning rates, batch sizes and weight initialisation. Our proposed system achieved its highest performance {--} a Pearson correlation coefficient of 0.750 {--} on the onversation-level empathy prediction task1 . The system implementation is publicly available at https: //github.com/hasan-rakibul/WASSA23-empathy-emotion.",
}
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<abstract>The WASSA 2023 shared task on predicting empathy, emotion and other personality traits consists of essays, conversations and articles in textual form and participants’ demographic information in numerical form. To address the tasks, our contributions include (1) converting numerical information into meaningful text information using appropriate templates, (2) summarising lengthy articles, and (3) augmenting training data by paraphrasing. To achieve these contributions, we leveraged two separate T5-based pre-trained transformers. We then fine-tuned pre-trained BERT, DistilBERT and ALBERT for predicting empathy and personality traits. We used the Optuna hyperparameter optimisation framework to fine-tune learning rates, batch sizes and weight initialisation. Our proposed system achieved its highest performance – a Pearson correlation coefficient of 0.750 – on the onversation-level empathy prediction task1 . The system implementation is publicly available at https: //github.com/hasan-rakibul/WASSA23-empathy-emotion.</abstract>
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%0 Conference Proceedings
%T Curtin OCAI at WASSA 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers
%A Hasan, Md Rakibul
%A Hossain, Md Zakir
%A Gedeon, Tom
%A Soon, Susannah
%A Rahman, Shafin
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F hasan-etal-2023-curtin
%X The WASSA 2023 shared task on predicting empathy, emotion and other personality traits consists of essays, conversations and articles in textual form and participants’ demographic information in numerical form. To address the tasks, our contributions include (1) converting numerical information into meaningful text information using appropriate templates, (2) summarising lengthy articles, and (3) augmenting training data by paraphrasing. To achieve these contributions, we leveraged two separate T5-based pre-trained transformers. We then fine-tuned pre-trained BERT, DistilBERT and ALBERT for predicting empathy and personality traits. We used the Optuna hyperparameter optimisation framework to fine-tune learning rates, batch sizes and weight initialisation. Our proposed system achieved its highest performance – a Pearson correlation coefficient of 0.750 – on the onversation-level empathy prediction task1 . The system implementation is publicly available at https: //github.com/hasan-rakibul/WASSA23-empathy-emotion.
%R 10.18653/v1/2023.wassa-1.47
%U https://aclanthology.org/2023.wassa-1.47
%U https://doi.org/10.18653/v1/2023.wassa-1.47
%P 536-541
Markdown (Informal)
[Curtin OCAI at WASSA 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers](https://aclanthology.org/2023.wassa-1.47) (Hasan et al., WASSA 2023)
ACL
- Md Rakibul Hasan, Md Zakir Hossain, Tom Gedeon, Susannah Soon, and Shafin Rahman. 2023. Curtin OCAI at WASSA 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 536–541, Toronto, Canada. Association for Computational Linguistics.